Stream Processing

Stream processing handles continuous flows of events in real-time. Instead of batch processing, agents analyze data as it arrives—computing aggregates, detecting patterns, and triggering actions based on windowed views of the stream.

This is essential for monitoring systems, real-time analytics, anomaly detection, and any application where immediate response to data patterns matters.

Interactive: Sliding Window Aggregator

Watch a live data stream and see how sliding windows compute real-time aggregations. Adjust window size and aggregation type to understand stream processing concepts.

Window Size: 5 events

Aggregation Type

EVENT STREAM

Start stream to see events flowing...

Window Types

Sliding Window

Moves continuously with each new event

Tumbling Window

Fixed-size, non-overlapping time buckets

Session Window

Groups events by periods of activity

Use Cases

📊

Real-Time Analytics

Compute metrics as data arrives

🚨

Anomaly Detection

Spot outliers in live data streams

💰

Fraud Detection

Identify suspicious patterns immediately

💡 Key Insight

Stream processing turns infinite data into bounded computations. By using windows, you can compute aggregates, averages, and patterns over unbounded event streams without storing everything. A sliding window of the last 1000 events lets you monitor "current behavior" while automatically forgetting old data—perfect for real-time systems.

Event-Driven Agents

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